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Authors: Ujjwal Verma 1 ; Florence Rossant 2 ; Isabelle Bloch 3 ; Julien Orensanz 4 and Denis Boisgontier 4

Affiliations: 1 ISEP, Télécom ParisTech and CNRS LTCI, France ; 2 ISEP, France ; 3 Télécom ParisTech and CNRS LTCI, France ; 4 Cap2020, France

ISBN: 978-989-758-018-5

Keyword(s): Image Segmentation, Parametric Active Contours, Shape Constraint, Precision Farming, Elliptic Approximation.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image Understanding ; Image-Based Modeling ; Object Recognition ; Pattern Recognition ; Shape Representation ; Software Engineering

Abstract: In this paper, we present a segmentation procedure based on a parametric active contour with shape constraint, in order to follow the growth of the tomatoes from the images acquired in the field. This is a challenging task because of the poor contrast in the images and the occlusions by the vegetation. In our sequential approach, considering one image per day, we assume that a segmentation of the tomatoes is available for the image acquired the previous day. An initial curve for the active contour model is computed by combining gradient information and region information. Then, an active contour with shape constraint is applied to provide an elliptic approximation of the tomato boundary. We performed a quantitative evaluation of our approach by comparing the results with the manual segmentation. Given the varying degree of occlusion in the images, the image data set was divided into three categories, based on the occlusion degree of the tomato in the processed image. For the cases wit h low occlusion, good results were obtained, with an average relative distance between the manual segmentation and the automatic segmentation of 2.73% (expressed as percentage of the size of tomato). For the images with significant amount of occlusion, a good segmentation was obtained on 44% of the images, where the average error was less than 10%. (More)

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Paper citation in several formats:
Verma, U.; Rossant, F.; Bloch, I.; Orensanz, J. and Boisgontier, D. (2014). Shape-based Segmentation of Tomatoes for Agriculture Monitoring.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 402-411. DOI: 10.5220/0004818804020411

@conference{icpram14,
author={Ujjwal Verma. and Florence Rossant. and Isabelle Bloch. and Julien Orensanz. and Denis Boisgontier.},
title={Shape-based Segmentation of Tomatoes for Agriculture Monitoring},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={402-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004818804020411},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Shape-based Segmentation of Tomatoes for Agriculture Monitoring
SN - 978-989-758-018-5
AU - Verma, U.
AU - Rossant, F.
AU - Bloch, I.
AU - Orensanz, J.
AU - Boisgontier, D.
PY - 2014
SP - 402
EP - 411
DO - 10.5220/0004818804020411

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